The field of the invention is methods of computer analysis for forewarning of critical events, such as epileptic seizures in human medical patients, and mechanical failures in machines and other physical processes.
Hively et al., U.S. Pat. Nos. 5,743,860 and 5,857,978 disclose methods for detecting and predicting epileptic seizures by acquiring brain wave data from a patient, and analyzing the data with traditional nonlinear methods.
Many of the prior art methods of epileptic forewarning were based on intercranial electroencephalogram (EEG) data. The present invention can be practiced with EEG data obtained from sensors applied to the scalp of the patient. Prior advances using scalp EEG data removed artifacts with a zero-phase quadratic filter to permit analysis of single-channel scalp EEG data. Hively et al., U.S. Pat. No. 5,815,413, disclosed the use of phase space dissimilarity measures (PSDM) to forewarn of impending epileptic events from scalp EEG in ambulatory settings. Despite noise in scalp EEG data, PSDM has yielded superior performance over traditional nonlinear indicators, such as Kolmogorov entropy, Lyapunov exponents, and correlation dimension. However, a problem still exists in forewarning indicators, because false positives and false negatives may occur.
Hively et al., U.S. Pat. No. 5,815,413, also discloses the applicability of nonlinear techniques to monitor machine conditions such as the condition of a drill bit or the performance of an electrical motor driving a pump.